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In wireless networks, drone base stations (DBSs) offer significant benefits in terms of Quality of Service (QoS) improvement due to their line-of-sight (LoS) transmission capabilities and adaptability. However, LoS links can suffer degradation in complex propagation environments, especially in urban areas with dense structures like buildings. As a promising technology to enhance the wireless communication networks, reconfigurable intelligent surfaces (RIS) have emerged in various Internet of Things (IoT) applications by adjusting the amplitude and phase of reflected signals, thereby improving signal strength and network efficiency. This study aims to propose a novel approach to enhance communication coverage and throughput for mobile ground users by intelligently leveraging signal reflection from DBSs using ground-based RIS. We employ Deep Reinforcement Learning (DRL) to optimize both the DBS location and RIS phase-shifts. Numerical results demonstrate significant improvements in system performance, including communication quality and network throughput, validating the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The author of this paper explored Vivaldi [1] [2] line theory and technology and used it as a basis to propose a Vivaldi antenna array to replace a single Vivaldi antenna. This was to achieve a dual-polarized antenna with high directivity and high isolation in the MIMO anechoic chamber, and one that is minimally affected by the environment. The operating frequency of this antenna array covers a frequency range of 0.7–6.0 GHz and is composed of four Vivaldi units with relatively high isolation between them to reduce measurement errors caused by coupling. In each unit, two identical Vivaldi antennas are connected in parallel to form the same polarization unit, and a microstrip power divider was used and the impedance matching of circular holes was performed to design this connected antenna with an ultra-wide operating frequency and the same polarization. The authors then interconnected two polarization units orthogonally at 90 degree cross to form the antenna described in this study, which has high directivity, high isolation, an ultra-wide frequency and dual polarization. During the design process, an FR4 printed circuit board (PCB) was used to effectively reduce the cross-sectional area of the antenna and reduce reflection and interference on the basis of ensuring an ultra-wide operating frequency. Additionally, the two orthogonal units of each polarized antenna unit had to work separately, and the electric field data collected from different polarization directions were sequentially transmitted to the receiver for postprocessing to satisfy the measurement requirements of the MIMO OTA anechoic chamber. In this study, SEMCAD electromagnetic simulation software was used to adjust and complete the analysis of antenna characteristics and obtain a favorable operating frequency and voltage standing wave ratio, as well as excellent isolation and radiation characteristics.

An accurate propagation channel model is crucial for evaluating the performance of a communication system. A propagation channel can be described by a Markov model with a finite number of states, each of which is considered to be quasi-stationary over a short period. This work proposes a two-layer multistate Markov model. Instead of a large Markov transition matrix used in a conventional single-layer Markov model, two small Markov transition matrices are employed by a two-layer Markov model to reduce the computational complexity of the model without increasing the memory requirements. The proposed approach characterizes the multiplicative processes of a propagation channel as shadowing and fast fading. Each type of fading is considered as several channel states and each of the states corresponds to a specific mixed Rayleigh-lognormal distribution. Numerical results reveal that the statistical properties of the simulated data are quite close to those obtained from the measurements; indeed, the proposed two-layer Markov model is more accurate and less complex, and requires less memory than the single-layer Markov model. Furthermore, the proposed two-layer Markov model enables the fading statistics and error probability performance of a quadrature phase-shift keying modulation scheme in a typical urban Taipei environment to be more accurately predicted. Besides, it can easily be applied to similar environmental scenarios. Index Terms--Markov model, propagation channel modeling.

Mobile users are interested in utilising high network capabilities without time and place constraints. However, with a high level of interest in the usage of mobile phones and internet facilities, the limited capacity of terrestrial base stations (BSs) is unbalanced. As a potential alternative to BSs, unmanned aerial vehicles (UAVs) are emerging as a means of transmitting wireless data to ground mobile users. As an air‐to‐ground communication network, the real UAVs deployed and collected communication data from ground mobile users. The main objective of this study is to analyse and evaluate user throughput, interference, and power transmission when the UAVs are at different heights. The parameters used include the locations of the UAVs and users, the altitudes and elevation angles from the users to UAVs, signal‐to‐noise‐ratio, throughput values, the categories of line‐of‐sight, and non‐line‐of‐sight links. Furthermore, K‐means used as a clustering method for class identification, long short‐term memory (LSTM), and gated recurrent unit (GRU) to analyse and evaluate system performance. The system's performance was compared with a multi‐layer perceptron approach. The evaluation results show that the proposed LSTM–GRU provides reliable and encouraging performance with low computational complexity, which is appropriate for heterogeneous networks. [ABSTRACT FROM AUTHOR]

A spatial signature is the response vector of a base-station antenna array to a mobile unit at a certain location. Mobile subscribers at different locations exhibit different spatial signatures. The exploitation of spatial diversity (or the difference of spatial signatures) is the basic idea behind the so-called space-division multiple-access (SDMA) scheme, which can be used to significantly increase the channel capacity and quality of a wireless communication system. Although SDMA schemes have been studied by a number of researchers [1]-[6], most of these studies are based on theoretical analyses and computer simulations with ideal assumptions. Not much experimental study [7], [8] has been reported on spatial signature variation due to nonideal perturbations in a real wireless communication environment. The purpose of this paper is to present, for the first time, extensive experimental results of spatial signature variation using a smart antenna testbed. The results to be presented include the spatial signature variation with time, frequency, small displacement, multipath angle spread and beamforming performance. The experimental results show the rich spatial diversity and potential benefits of using an antenna array for wireless communication applications. Index Terms - Adaptive arrays, mobile communication.

In wireless communications, smart antenna systems (or antenna arrays) can be used to suppress multipath fading with antenna diversity and to increase system capacity by supporting multiple co-channel users in reception and transmission. This paper presents experimental results of diversity gain, interference cancellation, and mitigation of multipath fading obtained by using a smart antenna system in typical wireless scenarios. Also given are experimental results for the signal-to-interference ratio (SIR) of two moving users, comparing different beamforming algorithms in typical wireless scenarios. All of the experiments were performed using the 900-MHz smart antenna testbed at The University of Texas at Austin. Index Terms - Antenna arrays, mobile antennas, multipath channels.

This paper presents results from simultaneous Land S-band slant-path fade measurements through a pecan, a cottonwood, and a pine tree employing a tower-mounted transmitter and dual-frequency receiver. A single circularly polarized antenna was used at each end of the link. The objective was to provide information for personal communications satellite up-link power control design on the correlation of tree shadowing between frequencies near 1620 and 2500 MHz. Fade time series obtained simultaneously did not exhibit any significant correlation, but their means were weakly correlated ([r.sup.2] from 0.31 to 0.58). Fades were measured along a 10-m lateral distance with 5-cm spacing. Instantaneous fade differences between L- and S-band exhibited a normal distribution with means usually near 0 dB and standard deviations from 5.2 to 7.5 dB. The cottonwood tree was an exception with 5.4 dB higher average fading at S-than at L-band. More than 90% of the spatial variations occurred with periods larger than 1 [similar to] 2 wavelengths. Simultaneous swept measurements over 160-MHz spans showed that the standard deviation of the power levels as function of frequency increased from about 1 dB at locations with mean fades less than 4 dB to near 6 dB at locations with mean fades of 20 dB. For a 5-dB fade, the central 90% of fade slopes were within a band of 0.7 dB/MHz at L- and 1.9 dB/MHz at S-band.

An accurate propagation channel model is crucial for evaluating the performance of a communication system. A propagation channel can be described by a Markov model with a finite number of states, each of which IS considered to be quasi-stationary over a short period. This work proposes a two-layer multistate Markov model. Instead of a large Markov transition matrix used in a conventional single-layer Markov model, two small Markov transition matrices are employed by a two-layer Markov model to reduce the computational complexity of the model without increasing the memory requirements. The proposed approach characterizes the multiplicative processes of a propagation channel as shadowing and fast fading. Each type of fading is considered as several channel states and each of the states corresponds to a specific mixed Rayleigh-lognormal distribution. Numerical results reveal that the statistical properties of the simulate data are quite close to those obtained from the measurements; indeed, the proposed two-layer Markov model is more accurate and less complex, and requires less memory than the single-layer Markov model. Furthermore, the proposed two-layer Markov model enables the fading statistics and error probability performance of a quadrature phase-shift keying modulation scheme in a typical urban Thipei environment to be more accurately predicted. Besides, it can easily be applied to similar environmental scenarios. [ABSTRACT FROM AUTHOR]

The use of unmanned aerial vehicles (UAVs) as a communication platform has great practical importance for future wireless networks, especially for on-demand deployment for temporary and emergency conditions. The user throughput estimation in a wireless system depends on the data traffic load and the available capacity to support that load. In UAV-assisted communication, the position of the UAV is one major factor that affects the capacity available to the data flows being served. This study applies multi-layer perceptron (MLP) and long short term memory (LSTM) approaches to determine the position of a UAV that maximizes the overall system performance and user throughput. To analyze and evaluate the system performance, we apply the hybrid of MLP-LSTM for classification regression tasks and K-means algorithms for automatic clustering of classes. The implementation of our work is done through TensorFlow packages. The performance of our proposed system is compared with other approaches to give accurate and novel results for both classification and regression tasks of the user throughput maximization and UAV positioning. According to the results, 98% of the user throughput maximization accuracy is correctly classified. Moreover, the UAV positioning provides accuracy levels of 94.73%, 98.33%, and 99.53% for original datasets (scenario 1), reduced features on the estimated values of user throughput at each grid point (scenario 2), and reduced feature datasets collected on different days and grid points achieved maximum throughput (scenario 3), respectively. [ABSTRACT FROM AUTHOR]

The use of unmanned aerial vehicles (UAVs) as a communication platform has great practical importance for future wireless networks, especially for on-demand deployment for temporary and emergency conditions. The user throughput estimation in a wireless system depends on the data traffic load and the available capacity to support that load. In UAV-assisted communication, the position of the UAV is one major factor that affects the capacity available to the data flows being served. This study applies multi-layer perceptron (MLP) and long short term memory (LSTM) approaches to determine the position of a UAV that maximizes the overall system performance and user throughput. To analyze and evaluate the system performance, we apply the hybrid of MLP-LSTM for classification regression tasks and K-means algorithms for automatic clustering of classes. The implementation of our work is done through TensorFlow packages. The performance of our proposed system is compared with other approaches to give accurate and novel results for both classification and regression tasks of the user throughput maximization and UAV positioning. According to the results, 98% of the user throughput maximization accuracy is correctly classified. Moreover, the UAV positioning provides accuracy levels of 94.73%, 98.33%, and 99.53% for original datasets (scenario 1), reduced features on the estimated values of user throughput at each grid point (scenario 2), and reduced feature datasets collected on different days and grid points achieved maximum throughput (scenario 3), respectively., Competing Interests: The authors declare no conflict of interest.